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Multi-Document Summarization

Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.

Source: Multi-Document Summarization using Distributed Bag-of-Words Model

Papers

Showing 101110 of 359 papers

TitleStatusHype
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization0
基於基因演算法的組合式多文件摘要方法 (An Ensemble Approach for Multi-document Summarization using Genetic Algorithms) [In Chinese]0
Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews0
End-to-end Argument Generation System in Debating0
Exploiting Timelines to Enhance Multi-document Summarization0
Entity-Supported Summarization of Biomedical Abstracts0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization0
Fast Joint Compression and Summarization via Graph Cuts0
Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model0
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